Bibliographic record
Abstract
Recently, there has been a push for courts to review rules made by the executive for substantive reasonableness. While reasonableness review may foster better-informed regulation, it also risks giving vested interests disproportionate influence over rulemaking. By flooding rulemakers with analyses emphasizing regulation’s costs and uncertainties about its benefits, to which rulemakers must then respond so as to survive reasonableness review, these interests can slow down and frustrate regulation designed to benefit the public. Courts could mitigate this risk, however, by applying reasonableness review in a way that recognizes the uncertainty that attends the rulemaking process—including the limits it imposes on rulemakers’ ability to refute alternative analyses of new rules’ likely costs and benefits. This does not mean acquiescing in arbitrary decision-making. To the extent rules’ effects are uncertain at adoption, courts can encourage rulemakers to revisit these rules post-implementation. Properly designed, reasonableness review can foster informed regulation that responds to new evidence and is less easily diverted from public-oriented objectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".